A Visual Guide to Tuning Gradient Boosted Trees

Gradient boosted trees are visually explored using scikit-learn's implementation on the California housing dataset. The process demonstrates how a model's predictions converge towards the true value over successive iterations. Key hyperparameters such as `max_depth`, `learning_rate`, and `n_estimators` are tuned, with their impact on performance metrics and prediction behavior illustrated through various plots. This analysis highlights the critical trade-off between the learning rate and the number of estimators required for the model to achieve accurate results.